Unifying 3D Vision-Language Understanding via Promptable Queries

Ziyu Zhu, Zhuofan Zhang, Xiaojian Ma, Xuesong Niu, Yixin Chen, Baoxiong Jia, Zhidong Deng📧, Siyuan Huang📧, Qing Li📧

This repository is the official implementation of the ECCV 2024 paper "Unifying 3D Vision-Language Understanding via Promptable Queries".

Paper | arXiv | Project | HuggingFace Demo | Checkpoints

News

• [ 2024.08 ] Release training and evaluation.
• [ 2024.07 ] Our huggingface DEMO is here DEMO, welcome to try our model!
• [ 2024.07 ] Release codes of model! TODO: Clean up training and evaluation

Abstract

A unified model for 3D vision-language (3D-VL) understanding is expected to take various scene representations and perform a wide range of tasks in a 3D scene. However, a considerable gap exists between existing methods and such a unified model, due to the independent application of representation and insufficient exploration of 3D multi-task training. In this paper, we introduce PQ3D, a unified model capable of using Promptable Queries to tackle a wide range of 3D-VL tasks, from low-level instance segmentation to high-level reasoning and planning.Tested across ten diverse 3D-VL datasets, This is achieved through three key innovations: (1) unifying various 3D scene representations (i.e., voxels, point clouds, multi-view im- ages) into a shared 3D coordinate space by segment-level grouping, (2) an attention-based query decoder for task-specific information retrieval guided by prompts, and (3) universal output heads for different tasks to support multi-task training. PQ3D demonstrates impressive performance on these tasks, setting new records on most benchmarks. Particularly, PQ3D improves the state- of-the-art on ScanNet200 by 4.9% (AP25), ScanRefer by 5.4% ([email protected]), Multi3DRefer by 11.7% ([email protected]), and Scan2Cap by 13.4% ([email protected]).Moreover, PQ3D supports flexible inference with individual or combined forms of available 3D representations, e.g., solely voxel input

Install

1. Install conda package

conda env create --name envname 
pip3 install torch==2.0.0
pip3 install torchvision==0.15.1
pip3 install -r requirements.txt

2. install pointnet2

cd modules/third_party
# PointNet2
cd pointnet2
python setup.py install
cd ..

3. Install Minkowski Engine

git clone https://github.com/NVIDIA/MinkowskiEngine.git
conda install openblas-devel -c anaconda
cd MinkowskiEngine
python setup.py install --blas_include_dirs=${CONDA_PREFIX}/include --blas=openblas

Prepare data

1. download sceneverse data from scene_verse_base and change data.scene_verse_base to sceneverse data directory.
2. download segment level data from scene_ver_aux and change data.scene_verse_aux to download data directory.
3. download other data from scene_verse_pred and change data.scene_verse_pred to download data directory.

Prepare checkpoints

1. download PointNet++ from pointnet and change pretrained_weights_dir to downloaded directory,
2. download checkpoint from stage1, stage2 and change pretrain_ckpt_path to certain checkpoint weight.

Run PQ3D

Stage 1 training for instance segmentation

python3 run.py --config-path configs --config-name instseg_sceneverse_gt.yaml

python3 run.py --config-path configs --config-name instseg_sceneverse.yaml pretrain_ckpt={ckpt_from_instseg_sceneverser_gt}

Stage 1 evaluation

python3 run.py --config-path configs --config-name instseg_sceneverse.yaml mode=test pretrain_ckp_path={pretrain_ckpt_path}

Stage 2 training for vl tasks

python3 run.py --config-path configs --config-name unified_tasks_sceneverse.yaml

Stage 2 evaluation

python3 run.py --config-path configs --config-name unified_tasks_sceneverse.yaml mode=test pretrain_ckpt_path={pretrain_ckpt_path}

For multi-gpu training usage, we use four GPU in our experiments.

python launch.py --mode ${launch_mode} \
    --qos=${qos} --partition=${partition} --gpu_per_node=4 --port=29512 --mem_per_gpu=80 \
    --config {config}  \

Acknowledgement

We would like to thank the authors of Vil3dref, Mask3d, Openscene, Xdecoder, and 3D-VisTA for their open-source release.

Citation:

@article{zhu2024unifying,
    title={Unifying 3D Vision-Language Understanding via Promptable Queries},
    author={Zhu, Ziyu and Zhang, Zhuofan and Ma, Xiaojian and Niu, Xuesong and Chen, Yixin and Jia, Baoxiong and Deng, Zhidong and Huang, Siyuan and Li, Qing},
    journal={arXiv preprint arXiv:2405.11442},
    year={2024}
}